Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

Fang Kuai; Shen Chaopeng; Kifer Daniel; Yang Xiao

摘要

Abstract >The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedules. Utilizing a state‐of‐the‐art time series deep learning neural network, Long Short‐Term Memory (LSTM), we created a system that predicts SMAP level‐3 moisture product with atmospheric forcings, model‐simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root‐mean‐square errors (0.035) and high‐correlation coefficients 0.87 for over 75% of Continental United States, including the forested southeast. As the first application of LSTM in hydrology, we show the proposed network avoids overfitting and is robust for both temporal and spatial extrapolation tests. LSTM generalizes well across regions with distinct climates and environmental settings. With high fidelity to SMAP, LSTM shows great potential for hindcasting, data assimilation, and weather forecasting. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“main”XML：ID =“Grl56563-Abs-0001”XML：Lang =“en”> <标题类型=“main”XML：Lang =“en”>抽象</ title> >自2015年以来，土壤湿气活性被动（SMAP）任务已经提供了表面土壤水分的宝贵感应。但是，它具有短时间跨度和不规则的Revisit计划。利用最先进的时间序列深度学习神经网络，长短短期记忆（LSTM），我们创建了一个系统，该系统预测了具有大气强制，模型模拟水分和静态地理学属性的SMAP水平-3水分产品作为输入。该系统消除了具有模型模拟的大部分偏差，并改善了预测的水分气候学，实现了小型测试根部平均误差（＆ 0.035）和高相关系数＆ 0.87超过美国大陆75％，包括森林东南。作为LSTM在水文中的第一次应用，我们显示所提出的网络避免过度装备，并且对于时间和空间外推测试是坚固的。 LSTM在具有不同气候和环境环境的地区遍历良好。具有高保真度的微量，LSTM显示出巨大的后传播，数据同化和天气预报潜力。</ p> </ abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-23539/'>《Geophysical Research Letters》</a> <b style="margin: 0 2px;">|</b><span>2017年第21期</span><b style="margin: 0 2px;">|</b><span>共10页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Fang Kuai&option=202" target="_blank" rel="nofollow">Fang Kuai;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Shen Chaopeng&option=202" target="_blank" rel="nofollow">Shen Chaopeng;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Kifer Daniel&option=202" target="_blank" rel="nofollow">Kifer Daniel;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Yang Xiao&option=202" target="_blank" rel="nofollow">Yang Xiao;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Department of Civil and Environmental EngineeringPennsylvania State UniversityUniversity Park PA USA;</p> <p>Department of Civil and Environmental EngineeringPennsylvania State UniversityUniversity Park PA USA;</p> <p>Department of Computer Science and EngineeringPennsylvania State UniversityUniversity Park PA USA;</p> <p>Department of Computer Science and EngineeringPennsylvania State UniversityUniversity Park PA USA;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/163.html" title="地球物理学">地球物理学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=SMAP&option=203" rel="nofollow">SMAP;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=deep learning&option=203" rel="nofollow">deep learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=LSTM&option=203" rel="nofollow">LSTM;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=soil moisture&option=203" rel="nofollow">soil moisture;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=hindcasting&option=203" rel="nofollow">hindcasting;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=remote sensing&option=203" rel="nofollow">remote sensing;</a> </p> <div class="translation"> 机译：Smap;深入学习;LSTM;土壤水分;Hindcasting;遥感; </div> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704019850261.html">Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Fang Kuai&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">Fang Kuai,</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Shen 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